What is Python Development?
Python is a popular programming language known for its simplicity, readability, and versatility. It is an interpreted language that is executed line by line rather than compiled. This makes writing and testing code more accessible, reducing turnaround time, and we get the app quickly.
Python can be used for various applications, including web development, data analysis, artificial intelligence, automation, and web scraping. Python has considerable community support which conducts good virtual events. Pycon is one of the popular events that happen around the world.
Django, Flask, FastAPI, Cherrypy, etc., are popular Python frameworks widely used for Python application development. Various libraries can be used in any framework to speed up development.
What type of applications can be developed in Python?
"Building a scalable application using Python is impossible" is a myth.
Many popular applications across different domains are built using Python. Let's see how Python fares in building different kinds of applications.
Web Development:
The most popular application built using Python is Instagram. It supports around 500 million users daily, which scraps the fact that Python needs to be more scalable. Another example in the field of entertainment would be Spotify.
Reddit and Quora are the most sought community platforms, and they are built using Python. Quora was built using the Django framework.
Regarding cloud storage, Dropbox has weight and is built using Python. A few applications built using other tech were later moved to Python, of which Youtube is a good example.
Artificial Intelligence
Artificial Intelligence is the ability of machines to perform tasks that require human-like intelligence. AI has been around for decades, but recent advances in computing power and neural networks have boosted the adoption of AI.
AI, as a whole, contains many things. Among those are Machine learning, Natural Language processing, deep learning, etc
TensorFlow, Keras, PyTorch, and Scikit-learn are popular libraries that make it the best choice for machine learning and artificial intelligence.
These days, there are frequent updates in AI; the latest sensation is the emergence of ChatGPT. It took the AI application to a whole new level. One can look at the latest updates by following a website https://paperswithcode.com/. It contains newly published papers, and one can also get the implementation for rapid prototyping.
Data Analysis is a significant part of Data Science, and some libraries ease this analysis like NumPy, Pandas, and Matplotlib. Pandas can connect to databases and read Excel and CSV files. It performs data cleaning and visualizes the same data using matplotlib and Seaborn. Dask is the library that is gaining popularity in data analysis.
Automation:
Python can be helpful in various automation activities like copying content from a website which we call web scraping. It can be performed using frameworks like Scrapy or libraries like Selenium and Beautiful Soup. Selenium can be used as an automation testing tool. It opens a browser using Chrome or firefox, where we can log in to a website and perform tasks.
Say we must perform a task requiring copying files from one location to another. For that, we can set a cron job daily. We can use Python's OS along with the shuttle library that can perform OS-level operations to copy and delete a file/folder from the system.
Top Python Web Frameworks
Python frameworks simplify developers' work as they contain basic things already set up. This pre-defined setup assists Python developers in focusing on core tasks or functionality, thereby improving productivity. Python has two types of frameworks
- Micro or Lightweight framework: Good for small and medium-sized applications
- Complete Stack framework: Good for large-scale application
Below are the most widely used frameworks in Python:
Django:
It is a Fullstack framework and the preferred choice for most organizations and developers as it includes most things by default. Django follows the Model View Controller architecture pattern. Any non-technical person can add or delete data from the app with Django's inbuilt admin module.
Django developers use the ORM layer to simplify database interactions rather than writing raw SQL queries.
Flask:
Flask is a microframework used for small applications. Unlike Django, Flask does not contain a database abstraction layer. It is a very minimal framework. Developers can use any external package conveniently rather than relying on inbuilt functionalities. For someone who wants to start learning Python to develop applications, Flask can be a perfect starting point.
It is also a popular framework in the data science community to deploy machine learning models in production.
FastAPI:
FastAPI has been gaining popularity recently due to its speed and performance, which is on par with nodejs and go language. FastAPI helped the Python language to overcome the myth that Python is slow in performance compared to other languages.
FastAPI uses the asynchronous nature of Python's asyncio library to handle many concurrent connections efficiently. Due to its performance, this framework is also gaining popularity in the Data Science community. Many developers are shifting to use FastAPI to build APIs quickly and have other frontend languages of choice.
Python Advantages
- Ease of Learning:
Python is an easy language to learn. It has a simple syntax. Any non-technical person wanting to start coding. They can take up Python and build applications or perform data analysis quickly.
- Large Community and Libraries:
Python is open source with a big community. If one is stuck developing any feature, one can get quick support from the community. Moreover, there might be a possibility that someone already has a solution to the problem. Developers have access to a vast pool of prebuilt libraries, so they must write all the code from scratch. Python has a package management system called Pip.
- Rapid Development
With the ease of writing code, Python helps organizations to create a prototype in a short time. Organizations can leverage to check the adoption of the concept and adapt to any changes very quickly.
Python Disadvantages
- Performance:
The simplicity of Python comes with the cost of speed. Since Python is an interpreted language, it executes each line of code step by step. Therefore the performance of Python could be better with other languages. Recent advancements overcome this in the FastAPI, where the performance is comparable with Nodejs and Go language.
- Mobile Development:
Python is rarely used for mobile development. There are frameworks like Kivy and BeeWare, but they need to be better established than other languages.
- Memory problems:
Python faces the problem of memory leaks; it consumes much memory compared to other languages.